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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
27/09/2022 |
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Actualizado : |
27/09/2022 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
ZARBÁ, L.; PIQUER-RODRÍGUEZ, M.; BOILLAT, S.; LEVERS, C.; GASPARRI, I.; AIDE, T. M.; ÁLVAREZ-BERRÍOS, N. L.; ANDERSON, L. O.; ARAOZ, E.; ARIMA, E.; BATISTELLA, M.; CALDERÓN-LOOR, M.; ECHEVERRÍA, C.; GONZALEZ-ROGLICH, M.; JOBBÁGY, E. G.; MATHEZ-STIEFEL, S.-L.; RAMIREZ-REYES, C-; PACHECHO, A.; VALLEJOS, M.; YOUNG, K. R.; GRAU, R. |
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Afiliación : |
LUCÍA ZARBÁ, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina.; MARÍA PIQUER-RODRÍGUEZ, Instituto Ecología Regional (IER), Univ. Nacional de Tucumán (UNT). Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; Lateinamerika-Institut, Freie Universität Berlin, Germany; Geography Department, Humbold, Germany; SÉBASTIEN BOILLAT, Institute of Geography, University of Bern, Bern, Switzerland; CHRISTIAN LEVERS, Depart. Environmental Geography, Inst. for Environmental Studies, Vrije Univ. Amsterdam, Netherlands; Inst. for Resources, Environment and Sustainability, Univ. of British Columbia, Vancouver, BC, Canada; School of Public Policy and Global Affairs, Univ.; IGNACIO GASPARRI, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; T. MITCHELL AIDE, Department of Biology, University of Puerto Rico-Rio Piedras, Puerto Rico; NORA L. ÁLVAREZ-BERRÍOS, USDA Forest Service, International Institute of Tropical Forestry, Río Piedras, Puerto Rico; LIANA O. ANDERSON, National Center for Monitoring and Early Warning of Natural Disasters-CEMADEN, Ministry of Science, Technology and Innovation-MCTI, Brazil; EZEQUIEL ARAOZ, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina; EUGENIO ARIMA, Department of Geography and the Environment, University of Texas at Austin, United States; MATEUS BATISTELLA, Brazilian Agricultural Research Corporation (Embrapa Agricultural Informatics) State University of Campinas (Unicamp), Brazil; MARCO CALDERÓN-LOOR, Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Melbourne, Australia;Grupo de Investigación de Biodiversidad, Medio Ambiente y Salud-BIOMAS, Universidad de las Américas (UDLA), Quito, Ecuador; CRISTIAN ECHEVERRÍA, Landscape Ecology Laboratory, Facultad de Ciencias Forestales, Universidad de Concepción, Chile; Millennium Nucleus Center for the Socioeconomic Impact of Environmental Policies (CESIEP), Santiago de Chile, Chile; MARIANO GONZALEZ-ROGLICH, Wildlife Conservation Society, Buenos Aires, Argentina; ESTEBAN G. JOBBÁGY, Grupo de Estudios Ambientales, IMASL-CONICET and Universidad Nacional de San Luis, San Luis, Argentina; South American Institute for Resilience and Sustainability Studies (SARAS), Maldonado, Uruguay; SARAH-LAN MATHEZ-STIEFEL, Centre for Development and Environment, University of Bern, Switzerland; Wyss Academy for Nature at the University of Bern, Switzerland; CARLOS RAMIREZ-REYES, Quantitative Ecology & Spatial Technologies Laboratory, Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, United States; ANDREA PACHECO, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany; MARÍA VALLEJOS, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, Universidad de Buenos Aires, Argentina; KENNETH R. YOUNG, Department of Geography and the Environment, University of Texas at Austin, United States; RICARDO GRAU, Instituto de Ecología Regional (IER), Universidad Nacional de Tucumán (UNT) Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Tucumán, Argentina. |
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Título : |
Mapping and characterizing social-ecological land systems of South America. |
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Fecha de publicación : |
2022 |
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Fuente / Imprenta : |
Ecology and Society, 2022, Volume 27, Issue 2, Article number 27. OPEN ACCESS. doi: https://doi.org/10.5751/ES-13066-270227 |
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ISSN : |
1708-3087 |
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DOI : |
10.5751/ES-13066-270227 |
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Idioma : |
Inglés |
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Notas : |
Article: Gold Open Access, Green Open Access. -- Erratum: On 6 June 2022 the abstract was edited. See online for more detail: https://ecologyandsociety.org/vol27/iss2/art27/#dataarchive_stmt --
LICENSE: Published under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license. -- Article metrics: https://plu.mx/plum/a/?doi=10.5751/ES-13066-270227&theme=plum-bigben-theme |
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Contenido : |
ABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world. Copyright © 2022 by the author(s). MenosABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide ran... Presentar Todo |
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Palabras claves : |
Automatization; Hierarchical clustering; Multidisciplinary data; Participatory mapping; Social-ecological mapping. |
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Asunto categoría : |
F01 Cultivo |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/16772/1/ES-2021-13066.pdf
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Marc : |
LEADER 03737naa a2200457 a 4500
001 1063581
005 2022-09-27
008 2022 bl uuuu u00u1 u #d
022 $a1708-3087
024 7 $a10.5751/ES-13066-270227$2DOI
100 1 $aZARBÁ, L.
245 $aMapping and characterizing social-ecological land systems of South America.$h[electronic resource]
260 $c2022
500 $aArticle: Gold Open Access, Green Open Access. -- Erratum: On 6 June 2022 the abstract was edited. See online for more detail: https://ecologyandsociety.org/vol27/iss2/art27/#dataarchive_stmt -- LICENSE: Published under license by The Resilience Alliance. This article is under a Creative Commons Attribution 4.0 International License. You may share and adapt the work provided the original author and source are credited, you indicate whether any changes were made, and you include a link to the license. -- Article metrics: https://plu.mx/plum/a/?doi=10.5751/ES-13066-270227&theme=plum-bigben-theme
520 $aABSTRACT.- Humans place strong pressure on land and have modified around 75% of Earth's terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world. Copyright © 2022 by the author(s).
653 $aAutomatization
653 $aHierarchical clustering
653 $aMultidisciplinary data
653 $aParticipatory mapping
653 $aSocial-ecological mapping
700 1 $aPIQUER-RODRÍGUEZ, M.
700 1 $aBOILLAT, S.
700 1 $aLEVERS, C.
700 1 $aGASPARRI, I.
700 1 $aAIDE, T. M.
700 1 $aÁLVAREZ-BERRÍOS, N. L.
700 1 $aANDERSON, L. O.
700 1 $aARAOZ, E.
700 1 $aARIMA, E.
700 1 $aBATISTELLA, M.
700 1 $aCALDERÓN-LOOR, M.
700 1 $aECHEVERRÍA, C.
700 1 $aGONZALEZ-ROGLICH, M.
700 1 $aJOBBÁGY, E. G.
700 1 $aMATHEZ-STIEFEL, S.-L.
700 1 $aRAMIREZ-REYES, C-
700 1 $aPACHECHO, A.
700 1 $aVALLEJOS, M.
700 1 $aYOUNG, K. R.
700 1 $aGRAU, R.
773 $tEcology and Society, 2022, Volume 27, Issue 2, Article number 27. OPEN ACCESS. doi: https://doi.org/10.5751/ES-13066-270227
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INIA Las Brujas (LB) |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
31/07/2017 |
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Actualizado : |
31/07/2017 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
FEITOSA, F. L. B.; OLIVIERI, B. F.; ABOUJAOUDE, C.; PEREIRA, A. S. C.; DE LEMOS, M. V. A.; CHIAIA, H. L. J.; BERTON, M. P.; PERIPOLLI, E.; FERRINHO, A. M.; MUELLER, L. F.; MAZZALI, M. R.; DE ALBUQUERQUE, L. G.; DE OLIVERA, H. N.; TONHATI, H.; ESPIGOLAN, R.; TONUSSI, R. L.; DE OLIVIERA SILVA, R. M.; GORDO, D. G. M.; MAGALHAES, A. F. B.; AGUILAR, I.; BALDI, F. S. B. |
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Afiliación : |
FABIELI LOISE BRAGA FEITOSA, UNESP-Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Sao Paulo, Brazil; BIANCA FERREIRA OLIVIERI, UNESP-Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Sao Paulo, Brazil; CAROLYN ABOUJAOUDE, UNESP-Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Sao Paulo, Brazil; ANGÉLICA SIMONE CRAVO, Faculdade de Medicina Veterinária e Zootecnia da USP, Pirassununga, Brazil; MARCOS VINICIUS ANTUNES DE LEMOS, UNESP-Universidade Estadual Paulista, Department of Animal Science, Sao Paulo, Brazil; HERMENEGILDO LUCAS JUSTINO CHIAIA, UNESP-Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Sao Paulo, Brazil; MARIANA PIATTO BERTON, UNESP-Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Sao Paulo, Brazil; ELISA PERIPOLLI, UNESP-Universidade Estadual Paulista, Department of Zootechnics, Sao Paulo, Brazil; ADRIELLE MATHIAS FERRINHO, Universidade de Sao Paulo - USP, Faculdade de Medicina Veterinária e Zootecnia, Sao Paulo, Brazil; LENISE FREITAS MUELLER, Universidade de Sao Paulo - USP, Faculdade de Zootecnia e Engenharia de Alimentos, Sao Paulo, Brazil; MÓNICA ROBERTA MAZALLI, Universidade de Sao Paulo - USP, Food Engineering Department, Sao Paulo, Brazil; LÚCIA GALVAO DE ALBUQUERQUE, UNESP-Universidade Estadual Paulista, Department of Animal Science, Sao Paulo, Brazil; HENRIQUE NUNES DE OLIVERA, UNESP-Universidade Estadual Paulista, Department of Animal Science, Sao Paulo, Brazil; HUMBERTO TONHATI, UNESP-Universidade Estadual Paulista, Department of Zootechnics, Sao Paulo, Brazil; RAFAEL ESPIGOLAN, UNESP-Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Sao Paulo, Brazil; RAFAEL LARA TONUSSI, UNESP-Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Sao Paulo, Brazil; RAFAEL MEDEIROS DE OLIVEIRA SILVA, UNESP-Universidade Estadual Paulista, Department of Animal Science, Sao Paulo, Brazil; DANIEL GUSTAVO MANSAN GORDO, UNESP-Universidade Estadual Paulista, Sao Paulo, Brazil; ANA FRABICIA BRAGA MAGALHAES, UNESP-Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Sao Paulo, Brazil; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO S. B. BALDI, UNESP-Universidade Estadual Paulista, Sao Paulo, Brazil. |
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Título : |
Genetic correlation estimates between beef fatty acid profile with meat and carcass traits in Nellore cattle finished in feedlot. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
Journal of Applied Genetics, 2017, 58 (1), 123-132. |
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ISSN : |
1234-1983 |
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DOI : |
10.1007/s13353-016-0360-7 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received: 15 December 2015 /Revised: 10 March 2016 /Accepted: 5 July 2016 / Published Online: 30 July 2016 |
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Contenido : |
ABSTRACT.
The objective of this study was to estimate the genetic?quantitative relationships between the beef fatty acid profile with the carcass and meat traits of Nellore cattle. A total of 1826 bulls finished in feedlot conditions and slaughtered at 24 months of age on average were used. The following carcass and meat traits were analysed: subcutaneous fat thickness (BF), shear force (SF) and total intramuscular fat (IMF). The fatty acid (FA) profile of the Longissimus thoracis samples was determined. Twenty-five FAs (18 individuals and seven groups of FAs) were selected due to their importance for human health. The animals were genotyped with the BovineHD BeadChip and, after quality control for single nucleotide polymorphisms (SNPs), only 470,007 SNPs from 1556 samples remained. The model included the random genetic additive direct effect, the fixed effect of the contemporary group and the animal?s slaughter age as a covariable. The (co)variances and genetic parameters were estimated using the REML method, considering an animal model (single-step GBLUP). A total of 25 multi-trait analyses, with four traits, were performed considering SF, BF and IMF plus each individual FA. The heritability estimates for individual saturated fatty acids (SFA) varied from 0.06 to 0.65, for monounsaturated fatty acids (MUFA) it varied from 0.02 to 0.14 and for polyunsaturated fatty acids (PUFA) it ranged from 0.05 to 0.68. The heritability estimates for Omega 3, Omega 6, SFA, MUFA and PUFA sum were low to moderate, varying from 0.09 to 0.20. The carcass and meat traits, SF (0.06) and IMF (0.07), had low heritability estimates, while BF (0.17) was moderate. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with BF were 0.04, 0.64 and −0.41, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with SF were 0.29, −0.06 and −0.04, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with IMF were 0.24, 0.90 and −0.67, respectively. The selection to improve meat tenderness in Nellore cattle should not change the fatty acid composition in beef, so it is possible to improve this attribute without affecting the nutritional beef quality in zebu breeds. However, selection for increased deposition of subcutaneous fat thickness and especially the percentage of intramuscular fat should lead to changes in the fat composition, highlighting a genetic antagonism between meat nutritional value and acceptability by the consumer.
© 2016, Institute of Plant Genetics, Polish Academy of Sciences, Poznan. MenosABSTRACT.
The objective of this study was to estimate the genetic?quantitative relationships between the beef fatty acid profile with the carcass and meat traits of Nellore cattle. A total of 1826 bulls finished in feedlot conditions and slaughtered at 24 months of age on average were used. The following carcass and meat traits were analysed: subcutaneous fat thickness (BF), shear force (SF) and total intramuscular fat (IMF). The fatty acid (FA) profile of the Longissimus thoracis samples was determined. Twenty-five FAs (18 individuals and seven groups of FAs) were selected due to their importance for human health. The animals were genotyped with the BovineHD BeadChip and, after quality control for single nucleotide polymorphisms (SNPs), only 470,007 SNPs from 1556 samples remained. The model included the random genetic additive direct effect, the fixed effect of the contemporary group and the animal?s slaughter age as a covariable. The (co)variances and genetic parameters were estimated using the REML method, considering an animal model (single-step GBLUP). A total of 25 multi-trait analyses, with four traits, were performed considering SF, BF and IMF plus each individual FA. The heritability estimates for individual saturated fatty acids (SFA) varied from 0.06 to 0.65, for monounsaturated fatty acids (MUFA) it varied from 0.02 to 0.14 and for polyunsaturated fatty acids (PUFA) it ranged from 0.05 to 0.68. The heritability estimates for Omega 3, Omega 6, SFA, MUFA and PUFA... Presentar Todo |
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Palabras claves : |
BOS INDICUS; FAT; HUMAN HEALTH; MEAT QUALITY; MEAT TENDERNESS; SELECTION. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 04086naa a2200469 a 4500
001 1057428
005 2017-07-31
008 2017 bl uuuu u00u1 u #d
022 $a1234-1983
024 7 $a10.1007/s13353-016-0360-7$2DOI
100 1 $aFEITOSA, F. L. B.
245 $aGenetic correlation estimates between beef fatty acid profile with meat and carcass traits in Nellore cattle finished in feedlot.$h[electronic resource]
260 $c2017
500 $aArticle history: Received: 15 December 2015 /Revised: 10 March 2016 /Accepted: 5 July 2016 / Published Online: 30 July 2016
520 $aABSTRACT. The objective of this study was to estimate the genetic?quantitative relationships between the beef fatty acid profile with the carcass and meat traits of Nellore cattle. A total of 1826 bulls finished in feedlot conditions and slaughtered at 24 months of age on average were used. The following carcass and meat traits were analysed: subcutaneous fat thickness (BF), shear force (SF) and total intramuscular fat (IMF). The fatty acid (FA) profile of the Longissimus thoracis samples was determined. Twenty-five FAs (18 individuals and seven groups of FAs) were selected due to their importance for human health. The animals were genotyped with the BovineHD BeadChip and, after quality control for single nucleotide polymorphisms (SNPs), only 470,007 SNPs from 1556 samples remained. The model included the random genetic additive direct effect, the fixed effect of the contemporary group and the animal?s slaughter age as a covariable. The (co)variances and genetic parameters were estimated using the REML method, considering an animal model (single-step GBLUP). A total of 25 multi-trait analyses, with four traits, were performed considering SF, BF and IMF plus each individual FA. The heritability estimates for individual saturated fatty acids (SFA) varied from 0.06 to 0.65, for monounsaturated fatty acids (MUFA) it varied from 0.02 to 0.14 and for polyunsaturated fatty acids (PUFA) it ranged from 0.05 to 0.68. The heritability estimates for Omega 3, Omega 6, SFA, MUFA and PUFA sum were low to moderate, varying from 0.09 to 0.20. The carcass and meat traits, SF (0.06) and IMF (0.07), had low heritability estimates, while BF (0.17) was moderate. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with BF were 0.04, 0.64 and −0.41, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with SF were 0.29, −0.06 and −0.04, respectively. The genetic correlation estimates between SFA sum, MUFA sum and PUFA sum with IMF were 0.24, 0.90 and −0.67, respectively. The selection to improve meat tenderness in Nellore cattle should not change the fatty acid composition in beef, so it is possible to improve this attribute without affecting the nutritional beef quality in zebu breeds. However, selection for increased deposition of subcutaneous fat thickness and especially the percentage of intramuscular fat should lead to changes in the fat composition, highlighting a genetic antagonism between meat nutritional value and acceptability by the consumer. © 2016, Institute of Plant Genetics, Polish Academy of Sciences, Poznan.
653 $aBOS INDICUS
653 $aFAT
653 $aHUMAN HEALTH
653 $aMEAT QUALITY
653 $aMEAT TENDERNESS
653 $aSELECTION
700 1 $aOLIVIERI, B. F.
700 1 $aABOUJAOUDE, C.
700 1 $aPEREIRA, A. S. C.
700 1 $aDE LEMOS, M. V. A.
700 1 $aCHIAIA, H. L. J.
700 1 $aBERTON, M. P.
700 1 $aPERIPOLLI, E.
700 1 $aFERRINHO, A. M.
700 1 $aMUELLER, L. F.
700 1 $aMAZZALI, M. R.
700 1 $aDE ALBUQUERQUE, L. G.
700 1 $aDE OLIVERA, H. N.
700 1 $aTONHATI, H.
700 1 $aESPIGOLAN, R.
700 1 $aTONUSSI, R. L.
700 1 $aDE OLIVIERA SILVA, R. M.
700 1 $aGORDO, D. G. M.
700 1 $aMAGALHAES, A. F. B.
700 1 $aAGUILAR, I.
700 1 $aBALDI, F. S. B.
773 $tJournal of Applied Genetics, 2017, 58 (1), 123-132.
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